Digital Plant Optimization Leader (AI & Analytics)

Magnum Ice Cream Co.

Covington (TN)

On-site

USD 103,000 - 154,000

Full time

3 days ago

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Benefits offered by this job

Health insurance

Retirement savings benefits

Life insurance and disability benefits

Parental leave

Paid vacation and holidays

Access to voluntary benefits

Job summary

Magnum Ice Cream Company is seeking a digital manufacturing leader to drive development, deployment, and scaling of manufacturing capabilities across our North American ice cream factory network. You will align factory priorities with data-driven initiatives, AI, predictive maintenance, and IT/OT integration to improve reliability, yield, and energy use.

The role requires hands-on experience with SCADA systems, historians, MES/MOM, and OT connectivity, plus cross-functional leadership across

Qualifications

Bachelor’s degree in engineering, manufacturing systems, computer science, data science, automation, IT, or related technical field; advanced degree preferred.
Manufacturing experience in digital transformation, engineering, industrial analytics, automation, IT/OT, or connected-factory environments.
Strong knowledge of OLE/OEE, downtime, waste, yield, throughput, reliability, maintenance, and capacity-loss analysis.
Hands-on experience with manufacturing data, databases or data warehousing, dashboards, visualization, and analytics tools.
Working knowledge of SCADA, historians, MES/MOM, PLC/HMI environments, sensors, industrial networks, and OT connectivity.
Familiarity with AI, machine learning, predictive maintenance, advanced analytics, simulation, or modeling in manufacturing.
Demonstrated ability to lead cross-functional programs, influence without direct authority, manage change, and deliver measurable results across multiple sites.

Responsibilities

Own the digital roadmap: Build and govern a prioritized North American roadmap aligned with factory losses, business needs, CapEx plans, and regional/global standards.
Turn data into action: Improve data acquisition, quality, structure, and access; deploy trusted dashboards and descriptive, diagnostic, predictive, and prescriptive analytics.
Scale AI and predictive maintenance: Develop value-led use cases using equipment, condition-monitoring, historian, SCADA, and maintenance data to reduce failures and improve asset performance.
Enable connected manufacturing: Partner with automation, controls, IT, OT, cybersecurity, engineering, and operations to integrate sensors, PLC/HMI/SCADA, historians, MES/MOM, and enterprise data platforms.
Deliver digital solutions: Lead requirements, design, testing, user acceptance, deployment, documentation, support, and continuous improvement for manufacturing applications.
Drive measurable performance: Use digital insights to improve downtime, waste, yield, throughput, reliability, capacity, quality, and energy performance.
Ensure governance and security: Apply approved architecture, cybersecurity, data privacy, responsible AI, access-control, and risk-review requirements; prevent unauthorized data or AI exposure.
Build capability and adoption: Train and coach operators, technicians, engineers, supervisors, and leaders; establish key-user models, playbooks, and sustainable support routines.